Search Results (119)

Freezing of gait (FoG) impairs mobility and daily functioning and increases the risk of falls, leading to a reduced quality of life (QoL) in people with Parkinson’s disease (PD). The Cue2walk, a wearable smart cueing device, can detect FoG and hereupon provides rhythmic cues to help people with PD manage FoG in daily life. This study investigated the user experiences and device usage of the Cue2walk, and its impact on health-related QoL, FoG and daily activities. Twenty-five users of the Cue2walk were invited to fill out an online survey, which included a modified version of the EQ-5D-5L, tailored to the use of the Cue2walk, and its scale for health-related QoL, three FoG-related questions, and a question about customer satisfaction. Sixteen users of the Cue2walk completed the survey. Average device usage per day was 9 h (SD 4). Health-related QoL significantly increased from 5.2/10 (SD 1.3) to 6.2/10 (SD 1.3) (p = 0.005), with a large effect size (Cohen’s d = 0.83). A total of 13/16 respondents reported a positive effect on FoG duration, 12/16 on falls, and 10/16 on daily activities and self-confidence. Customer satisfaction was 7.8/10 (SD 1.7). This pilot study showed that Cue2walk usage per day is high and that 15/16 respondents experienced a variety of positive effects since using the device. To validate these findings, future studies should include a larger sample size and a more extensive set of questionnaires and physical measurements monitored over time. Full article

This study aims to assess the stability of silica gel/polymer composites designed for open-cycle air dehumidification, humidification, and heat storage by employing a comprehensive set of characterization methods. To evaluate their resistance to various environmental factors, the materials were subjected to a series of aging treatments: (i) repeated adsorption/desorption cycles under representative operational conditions; (ii) post-drying at 30 °C, 40 °C, and 60 °C; (iii) immersion in water for 30 days; (iv) exposure to a salt–fog environment for 30 days; and (v) accelerated aging by alternation between wet and dry cycles. Prolonged exposure to liquid water significantly reduced the material’s stability, resulting in an 83% reduction in tensile strength after 30 days of immersion. However, discontinuous exposure to liquid water at low drying temperatures did not critically affect the material’s mechanical properties during wet/dry cycles. Furthermore, post-drying (performed at 22 °C and 50% RH) allows the recovery of mechanical performance, with a tensile strength reached comparable to those of the unaged composites. Similarly, adsorption/desorption cycles in water vapor did not trigger degradation in the material, with its water vapor adsorption capacity remaining comparable to the unaged material after 100 cycles. The results confirm the reliability of these composite materials as to their potential uses in open-cycle dehumidification, humidification, and heat-storage applications. Full article

Background/Objectives: This study investigated the prevalence of functional impairments and the effects of long COVID on long-term functional impairments in patients with severe COVID-19. Methods: We conducted a nationwide multicenter cohort study in collaboration with nine hospitals, collecting data using self-administered questionnaires from participants aged 20 years or older who were diagnosed with COVID-19, admitted to the intensive care unit (ICU) between April 2021 and September 2021, and discharged alive. Questionnaires regarding daily life, sequela, and functional impairments were mailed to patients in August 2022. The effects of long COVID on functional impairments were examined using a multivariate logistic regression analysis. Results: The survey was completed by 220 patients, with a mean of 416 days after discharge. Among respondents, 20.5% had physical impairments (n = 45), 35.0% had mental disorders (n = 77), and 42.7% had either (n = 94). Furthermore, 77.7% had long COVID (171/220), and the most common symptom was dyspnea (40.0%). The multivariate analysis showed that fatigue/malaise, upper respiratory tract symptoms, myalgia, muscle weakness, decreased concentration, sleep disorder, brain fog, and dizziness were risk factors for functional impairments at one year. Conclusions: Many patients with severe COVID-19 admitted to the ICU still suffered from post-intensive care syndrome even after one year, which manifested in combination with direct symptoms of the original disease, such as long COVID. Full article

Since the fiber optic gyroscope (FOG) is rigidly strapped down to the earth’s crust, there are various errors that affect the universal time (UT1) measurements. In this paper, the errors caused by various physical factors and mechanisms are analyzed in detail, with precession and nutation errors being taken into account, and modeling of the observation equations based on precession and nutation error correction is proposed. The mapping relationship with UT1 is established based on this observation equation; after the corresponding error correction and VLBI calibration, the high-accuracy solution of UT1 is finally completed. Through 14-day measurement experiments under a room temperature environment without any vibration isolation and magnetic shielding devices, the error variation of UT1 solution compared with the earth orientation parameter (EOP) 14 C04 data is calculated at less than 3.57 ms, with UT1 solution accuracy improved by 56% compared with the traditional method. These results indicate that this work facilitates the study of giant FOG error modeling and correction, advancing our understanding of errors in giant FOG measurements and improving the accuracy of UT1 solution. Full article

The Arctic Seas are attractive for shipping, fisheries, and other marine activities due to the abundant resources of the Arctic. The shrinking ice cover allows for the opening of activities in increasingly larger areas of the Arctic. This paper evaluates the possibility of executing all-year complex marine activities, here termed “marine operations”, in the Norwegian Sea and the ice-free part of the Barents Sea. The approach used during the preparation of this review paper is to identify constraints to marine operations so users can be aware of the limitations of performing such operations. The weather conditions in the Norwegian Sea and the Barents Sea are well known, and these seas are considered representative of ice-free or partly ice-free Arctic Seas with considerable marine activities. Similar conditions could be expected for other Arctic Seas during periods without ice cover. Marine operations require safe and stable working conditions for several days. The characteristics of marine operations are discussed, and the particulars of the Norwegian Sea and the Barents Sea physical environments are highlighted. Emphasis is on the wind and wave conditions in unpredictable polar low-pressure situations. Furthermore, situations with fog are discussed. The large uncertainties in forecasting the initiation and the tracks of polar lows represent the main concern for executing marine operations all year. Improvements in forecasting the occurrence and the path of polar lows would extend the weather window when marine operations could be carried out. Discussions of the potential for similar conditions in the wider Arctic Seas during ice-free periods are presented. Full article

Cloud computing has recently gained widespread attention owing to its use in applications involving the Internet of Things (IoT). However, the transmission of massive volumes of data to a cloud server often results in overhead. Fog computing has emerged as a viable solution to address this issue. This study implements an Artificial Intelligence of Things (AIoT) system based on fog computing on a smart farm. Three experiments are conducted to evaluate the performance of the AIoT system. First, network traffic volumes between systems employing and not employing fog computing are compared. Second, the performance of the communication protocols—hypertext transport protocol (HTTP), message queuing telemetry transport protocol (MQTT), and constrained application protocol (CoAP)—commonly used in IoT applications is assessed. Finally, a convolutional neural network-based algorithm is introduced to determine the maturity level of coffee tree images. Experimental data are collected over ten days from a coffee tree farm in the Republic of Korea. Notably, the fog computing system demonstrates a 26% reduction in the cumulative data volume compared with a non-fog system. MQTT exhibits stable results in terms of the data volume and loss rate. Additionally, the maturity level determination algorithm performed on coffee fruits provides reliable results. Full article

The detection and surveillance of ship targets in coastal waters is not only a crucial technology for the advancement of ship intelligence, but also holds great significance for the safety and economic development of coastal areas. However, due to poor visibility in foggy conditions, the effectiveness of ship detection in coastal waters during foggy weather is limited. In this paper, we propose an improved version of YOLOv8s, termed YOLOv8s-Fog, which provides a multi-target detection network specifically designed for nearshore scenes in foggy weather. This improvement involves adding coordinate attention to the neck of YOLOv8 and replacing the convolution in C2f with deformable convolution. Additionally, to expand the dataset, we construct and synthesize a collection of ship target images captured in coastal waters on days with varying degrees of fog, using the atmospheric scattering model and monocular depth estimation. We compare the improved model with the standard YOLOv8s model, as well as several other object detection models. The results demonstrate superior performance achieved by the improved model, achieving an average accuracy of 74.4% (mAP@0.5), which is 1.2% higher than that achieved by the standard YOLOv8s model. Full article

This study utilizes six years of hourly meteorological data from seven observation stations in the Beibu Gulf—Qinzhou (QZ), Fangcheng (FC), Beihai (BH), Fangchenggang (FCG), Dongxing (DX), Weizhou Island (WZ), and Hepu (HP)—over the period from 2016 to 2021. It examines the diurnal variations of sea fog occurrence and compares the performance of three machine learning (ML) models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost)—in predicting visibility associated with sea fog in the Beibu Gulf. The results show that sea fog occurs more frequently during the nighttime than during the daytime, primarily due to day-night differences in air temperature, specific humidity, wind speed, and wind direction. To predict visibility associated with sea fog, these variables, along with temperature-dew point differences ($T_{\mathrm{a}}-T_{\mathrm{d}}$), pressure (p), month, day, hour, and wind components, were used as feature variables in the three ML models. Although all the models performed satisfactorily in predicting visibility, XGBoost demonstrated the best performance among them, with its predicted visibility values closely matching the observed low visibility in the Beibu Gulf. However, the performance of these models varies by station, suggesting that additional feature variables, such as geographical or topographical variables, may be needed for training the models and improving their accuracy. Full article

This paper introduces an anchorless deep learning model designed for efficient analysis and processing of large-scale 3D synthetic traffic sign board datasets. With an ever-increasing emphasis on autonomous driving systems and their reliance on precise environmental perception, the ability to accurately interpret traffic sign information is crucial. Our model seamlessly integrates object detection, depth estimation, deformable parts, and text character extraction functionalities, facilitating a comprehensive understanding of road signs in simulated environments that mimic the real world. The dataset used has a large number of artificially generated traffic signs for 183 different classes. The signs include place names in Japanese and English, expressway names in Japanese and English, distances and motorway numbers, and direction arrow marks with different lighting, occlusion, viewing angles, camera distortion, day and night cycles, and bad weather like rain, snow, and fog. This was done so that the model could be tested thoroughly in a wide range of difficult conditions. We developed a convolutional neural network with a modified lightweight hourglass backbone using depthwise spatial and pointwise convolutions, along with spatial and channel attention modules that produce resilient feature maps. We conducted experiments to benchmark our model against the baseline model, showing improved accuracy and efficiency in both depth estimation and text extraction tasks, crucial for real-time applications in autonomous navigation systems. With its model efficiency and partwise decoded predictions, along with Optical Character Recognition (OCR), our approach suggests its potential as a valuable tool for developers of Advanced Driver-Assistance Systems (ADAS), Autonomous Vehicle (AV) technologies, and transportation safety applications, ensuring reliable navigation solutions. Full article

This research examined the response of flax-fiber-reinforced composites (FFRCs) to simulated outdoor conditions involving repeated exposure to salt fog and drying. The study investigated the effect of cycles on the toughness of the FFRCs. To achieve this, the composites were exposed to humidity (salt fog) for 10 days, followed by 18 days of drying in cycles. A total of up to 3 cycles, each lasting 4 weeks, were conducted over a 12-week period. Throughout this process, changes in the material’s weight, water absorption, and mechanical properties were monitored by water uptake and three-point bending tests. The findings revealed the significant impact of these humid–dry cycles on the mechanical response of the FFRCs. When exposed to humid environments without drying, the composite’s toughness increased significantly, due to a weakening effect more pronounced for stiffness, with strength reductions of about 20%. However, subsequent drying partially restored the material’s performance. After 18 days of drying, the composite regained most of its initial performance. Full article

Parking occupancy is difficult in most modern cities because of increases in the accessibility and use of motor vehicles, and users generally take several minutes or even hours to find a place to park. In this work, we propose a smart parking prediction model in order to help users locate in advance the availability of parking near the places they plan to visit. For this it is proposed a fog computing architecture that integrates a machine learning algorithm based on AdaBoost to predict parking places hours or days in advance. Additionally, a user interface was developed, which involves the collection of user inputs through a mobile application where the user is prompted to enter the destination location and the prediction time interval. Through extensive experimentation using real-world parking flow data, our proposed algorithm demonstrated an improved level of accuracy compared with alternative prediction methods. Moreover, a simulation was conducted to evaluate the system’s latency when using cloud computing versus our hybrid approach combining both fog and cloud computing. The results showed that employing the fog module in conjunction with cloud computing significantly reduced response delay in comparison with using cloud computing alone. Full article

Natural resources must be administered efficiently to reduce the human footprint and ensure the sustainability of the planet. Water is one of the most essential resources in agriculture. Modern information technologies are being introduced in agriculture to improve the performance of agricultural processes while optimizing water usage. In this scenario, artificial intelligence techniques may become a very powerful tool to improve efficiency. The introduction of the edge/fog/cloud paradigms, already adopted in other domains, may help to organize the services involved in complex agricultural applications. This article proposes the combination of several modern technologies to improve the management of hydrological resources and reduce water waste. The selected technologies are (1) fuzzy logic, used for control tasks since it adapts very well to the nonlinear nature of the agricultural processes, and (2) long range (LoRa) technology, suitable for establishing large distance links among the field devices (sensors and actuators) and the process controllers, executed in a centralized way. The presented approach has been validated in the laboratory by means of a control scheme aimed at achieving an adequate moisture level in the soil. The control algorithm, based on fuzzy logic, can use the weather forecast, obtained as a cloud service, to reduce water consumption. For testing purposes, the dynamics of the water balance model of the soil were implemented as hardware in the loop, executed in a dSPACE DS1104. Experiments proved the viability of the presented approach since the continuous space state output controller achieved a water loss reduction of 23.1% over a 4-day experiment length compared to a traditional on/off controller. The introduction of cloud services for weather forecasting improved the water reduction by achieving an additional reduction of 4.07% in water usage. Full article

Today, the IoT has become a vital part of our lives because it has entered into the precise details of human life, like smart homes, healthcare, eldercare, vehicles, augmented reality, and industrial robotics. Cloud computing and fog computing give us services to process IoT tasks, and we are seeing a growth in the number of IoT devices every day. This massive increase needs huge amounts of resources to process it, and these vast resources need a lot of power to work because the fog and cloud are based on the term pay-per-use. We make to improve the performance and cost (PC) algorithm to give priority to the high-profit cost and to reduce energy consumption and Makespan; in this paper, we propose the performance and cost–gray wolf optimization (PC-GWO) algorithm, which is the combination of the PCA and GWO algorithms. The results of the trial reveal that the PC-GWO algorithm reduces the average overall energy usage by 12.17%, 11.57%, and 7.19%, and reduces the Makespan by 16.72%, 16.38%, and 14.107%, with the best average resource utilization enhanced by 13.2%, 12.05%, and 10.9% compared with the gray wolf optimization (GWO) algorithm, performance and cost algorithm (PCA), and Particle Swarm Optimization (PSO) algorithm. Full article

Fog conditions at the offshore wind farm Horns Rev 2 were photographed on 16 April 2018. In this study, we present the results of an analysis of the meteorological conditions on the day of the photographs. The aim of the study was to examine satellite images, meteorological observations, wind turbine data, lidar data, reanalysis data, and wake and blockage model results to assess whether wind farm blockage was a likely cause for the formation of fog upstream of the wind farm. The analysis indicated the advection of warm and moist air mass from the southwest over a cool ocean, causing cold sea fog. Wind speeds at hub height were slightly above cut-in, and there was a strong veer in the shallow stable boundary layer. The most important finding is that the wake and blockage model indicated stagnant air mass arcs to the south and west of the wind farm. In the photographs, sea fog is visible in approximately the same area. Therefore, it is likely that the reduced wind triggered the sea fog condensation due to blockage in this area. A discrepancy between the blockage model and sea fog in the photographs appears in the southwest direction. Slightly higher winds might have occurred locally in a southwesterly direction, which may have dissolved sea fog. The wake model predicted long and narrow wind turbine wakes similar to those observed in the photographs. The novelty of the study is new evidence of wind farm blockage. It fills the gap in knowledge about flow in wind farms. Implications for future research include advanced modeling of flow phenomena near large offshore wind farms relevant to wind farm operators. Full article

Under the background of climate change, the Northeast Passage’s navigability is on the rise. Arctic sea fog significantly influences navigational efficiency in this region. Existing research primarily focuses on routes accumulating the lowest distance, neglecting routes with the lowest time and sea fog’s influence on route planning and navigational efficiency. This study compares the fastest and shortest routes and analyzes Arctic sea fog’s impact on the Northeast Passage from June to September (2001–2020). The results show that coastal areas are covered with less sea ice under notable monthly variations. Sea fog frequency is highest near coasts, declining with latitude. September offers optimal navigation conditions due to minimal ice and fog. When only sea ice is considered, the fastest route is approximately 4 days quicker than the shortest. The shortest route has migrated towards the higher latitude over two decades, while the fastest route remains closer to the Russian coast. Adding the impact of sea fog on the fastest route, the speed decreased by 30.2%, increasing sailing time to 45.1%. The new fastest route considering both sea ice and sea fog achieved a 13.9% increase in sailing speed and an 11.5% reduction in sailing time compared to the original fastest route. Full article